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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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modern models beyond the worst case e.g. integrating machine learning into algorithm design. We are looking for candidates with a strong mathematical background, an excellent degree in mathematics
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mathematical modeling to simulate water fluxes and biogeochemical processes related to carbon and nitrogen cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset
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) in materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials
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) in materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials
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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University
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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University
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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University
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of the German Armed Forces Munich), the DLR (German Aerospace Center) with its Oberpfaffenhofen institutes, and the BHL, the Bauhaus Luftfahrt. This pooling of research, graduate programmes and teaching merges